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16.
The Double Rainbow When it comes to “numbers”, the search language rocks! In social, what people “mean” matters. For that you’ll need some new tools that understand words and language “…what does it mean?!” 16

21.
Brand Perception Impacts Stock In 2011, our friends at Netﬂix announced that it would be increasing its subscription prices. The feedback on its Facebook page was outrage and the impact on its stock price was dramatic. 21

22.
Sentiment complements and informs “We analyze several surveys on consumer conﬁdence and political opinion over the 2008 to 2009 period, and ﬁnd they correlate to sentiment word frequencies in contemporaneous Twitter messages… …as high as 80%, and capture important large- scale trends. The results highlight the potential of text streams as a substitute and supplement for traditional polling.” From Tweets to Polls: Linking Text SenOment to Public Opinion Time Series (CMU: OConnor, Balasubramanyan, Routledge, and Smith 2010) 22

24.
Box Oﬃce Revenue Forecasting“We use the chatter from Twitter.com to forecast box-oﬃcerevenues for movies. We show that a simple model built fromthe rate at which tweets are created about particular topicscan outperform market-based predictors. We furtherdemonstrate how sentiments extracted from Twitter can befurther utilized to improve the forecasting power of socialmedia.” Asur and Huberman 2010 24

26.
What’s in a word?Terms have many contextdependent meanings." depend on the writer, the reader, and their relationship, history, goals and preferences" “unpredictable” bad in general, but good in movie reviews." “jobs” data was aﬀected by iPhone release 26

27.
How are you feeling right now? Plutchiks Wheel of Emotions Ekman’s Six Basic Emotions 27

28.
Sentiment analysis gonewrongWhen Anne Hathaway is mentioned, it’salmost always in a positive context, andas a result some trading algorithmsseem to purchase Berkshire Hathaway.When she is mentionedin the news, the stockgoes up. 28

30.
Bags of Words and Phrases Many sentiment words and expressions are not directly inﬂuenced by what is around them: That was fun :)But certainly they can be! They said it would be wonderful, but they were wrong. This "wonderful" movie turned out to be boring. 30

34.
The Eﬀect of Negation“The food was not good”Strategies: Negatingsentiment for all terms up to abreaking punctuation (i.e.,comma or sentence end)Negation eﬀect is dependenton the term. • Mild words negate about the same: not bad ≈ good • Extreme words negate towards neutral: not horrible ≈ average 34

35.
Learning BiasA common feature of online user-­‐supplied reviews is that the posiOve More occurrencesreviews vastly out-­‐number the negaOve ones. Movie reviews at IMDB: of “bad” in 10-star reviews than in 2- star ones. Normalize by accounting for relative frequencies. 35

36.
Sentiment in Social Media" Emoticons: :-) ;( :/ – Reliable measure of sentiment – Simple regex can cover more than 95% of emoticons on twitter – Ignores complex emotions" Lengthening – This talk is greeeeeat! David is the beeeeeeest! Ahhhhhhhhh! – In English 3 or more of the same char in a row doesn’t exist, except for 7 obscure terms in unix dict. – Can indicate heightened emotion, but actual lengths are probably not meaningful. – Useful to normalize because of how common they are (hiiii è hi) 36

37.
Maybe it’s not so hard?“We are only interested in aggregatesentiment. A high error rate merely impliesthe sentiment detector is a noisymeasurement instrument. With a fairlylarge number of measurements, theseerrors will cancel out relative to thequantity we are interested in estimating… From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series 37

39.
Design Decisions • Use supervised learning. Why? Doesn’t require interactive feedback. Learning get almost the best they are going to do with only a few hundred or perhaps a few thousand documents• Use naïve bayes. Why? Dirt simple and understandable. The diﬀerence between the best algorithms and a simple naïve bayes is generally only a few percent. 39

41.
Summary• Sentiment analysis helps you understand your customers and marketplace.• True sentiment analysis is hard.• Aggregate sentiment analysis is easier but still very valuable.• The simplest algorithms work almost as well as the most complex, given a few thousand training points.• Splunk has a Sentiment App. • Download it and give feedback. • Integrate Social data into your existing corporate data • Share your trained models with others. 41